Digital auto frequency control for a general purpose if subsystem with multi-modulation schemes

ABSTRACT

An automatic frequency control (AFC) device is provided. The AFC device includes an input module, a received signal strength indicator (RSSI) module and a carrier frequency offset (CFO) estimation module. The input module down converts and samples a received signal. The RSSI module is coupled to the input module and calculates a RSSI signal in response to the down converted and sampled received signal. The CFO estimation module is coupled to the input module and the RSSI module and calculates a moving average of binary elements of the down converted and sampled received signal. The CFO estimation module continues to calculate the moving average until the AFC converges.

PRIORITY CLAIM

This application is a U.S. national stage application filed under 35U.S.C. § 371 from International Application Serial No.PCT/SG2014/000079, which was filed 24 Feb. 2014, and published asWO2014/129978 on 28 Aug. 2014, and which claims priority to SingaporePatent Application No. 201301351-1, filed 22 Feb. 2013, whichapplications and publication are incorporated by reference as ifreproduced herein and made a part hereof in their entirety, and thebenefit of priority of each of which is claimed herein.

FIELD OF THE INVENTION

The present invention generally relates to digital automatic frequencycalibration (AFC) circuits, and more particularly relates to a digitalAFC circuit for use in a general purpose intermediate frequency (IF)subsystem with multi-modulation schemes.

BACKGROUND

Many wireless channel digital frequency and phase modulation systemssuch as frequency shift keying (FSK), Gaussian frequency shift keying(GFSK), and minimum phase shift keying (MSK) are sensitive to carrierfrequency offset (CFO) caused by transceiver oscillator instabilityand/or Doppler shift. This is especially true when data is transmittedin a burst mode. One possible solution applies an auto-frequencycalibration (AFC) block in the receiver to automatically estimate andcompensate for such frequency offset. However, compared with themultiplicity of conventional designs for FSK/GFSK/MSK transceivers, CFOestimation and compensation circuits for such systems are rare.

Conventional CFO estimation and/or compensation schemes have manydrawbacks. An early conventional scheme utilized a set of analog AFCtracking algorithms and can be recognized as the basis of the moderndigital AFC. Some conventional digital schemes utilized a set of digitalclosed-loop decision-aided AFC tracking algorithms for GFSK systems.However, both of these algorithms require reconstruction of transmitteddata symbols and submission of these data symbols to the CFO estimatoras reference information. Therefore, the trackable AFC range of theseconventional schemes is limited so as to not exceed the maximumfrequency divination and, thus, accurate sample timing recovery isrequired. Another typical scheme utilizes an open-loop AFC trackingalgorithm which directly estimates the DC offset of the discriminatoroutput. However, application of this AFC algorithm is limited tofrequency modulation systems with discriminator demodulators. A directCFO estimator based on received signals and remodulated transmittedsymbols has also been proposed, but the channel response and thetraining sequence must be known in advance. Further, some conventionalAFC algorithms are based on Fast Fourier Transforms (FFT) and MaximumLikelihood which have disadvantageous high computational requirements.

Additionally, many of the existing AFC algorithms assume that thereceived signal has a constant envelope. However, this assumption is notalways true, especially when the Inter-Channel Interference (ICI) andAutomatic Gain Control (AGC) uncertainties are taken into account. Afurther normalization method for AFC in GFSK systems normalizes theestimated CFO to the maximum deviation, ignoring gains along thereceiving path.

Thus, what is needed is an easy to implement automatic frequencycalibration scheme which does not require timing recovery and/or sourcedata recovery while also taking into account the ICI and AGCuncertainties. Furthermore, other desirable features and characteristicswill become apparent from the subsequent detailed description and theappended claims, taken in conjunction with the accompanying drawings andthis background of the disclosure.

SUMMARY

According to the Detailed Description, an automatic frequency control(AFC) device is provided. The AFC device includes an input module, areceived signal strength indicator (RSSI) module, and a carrierfrequency offset (CFO) estimation module. The input module down convertsand samples a received signal. The RSSI module is coupled to the inputmodule and calculates a RSSI signal in response to the down convertedand sampled received signal. The CFO estimation module is coupled to theinput module and the RSSI module and calculates a moving average ofbinary elements of the down converted and sampled received signal whenthe RSSI signal exceeds a predetermined threshold. The CFO estimationmodule continues to calculate the moving average until the AFCconverges.

In accordance with another aspect, a method for automatic frequencycontrol (AFC) is provided. The method includes monitoring a receivedstrength signal indicator (RSSI) signal and calculating a moving averageof binary elements of the received signal when the RSSI signal exceeds apredetermined threshold. The method further includes continuing the stepof calculating the moving average until the AFC converges.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying figures, where like reference numerals refer toidentical or functionally similar elements throughout the separate viewsand which together with the detailed description below are incorporatedin and form part of the specification, serve to illustrate variousembodiments and to explain various principles and advantages inaccordance with a present embodiment.

FIG. 1 depicts a block diagram of an intermediate frequency (IF)subsystem utilizing an automatic frequency calibration (AFC) circuit inaccordance with a present embodiment.

FIG. 2 depicts a graph of discriminator output of normalized I and Qchannel samples comparing discriminator output with a normalized inputin accordance with the present embodiment to discriminator output with anon-normalized input.

FIG. 3 depicts a flowchart of the operation of the normalization schemeof the AFC circuit depicted in FIG. 1 in accordance with the presentembodiment.

FIG. 4 depicts a graph of tracking logs of the AFC circuit of FIG. 1 atdifferent settings in accordance with the present embodiment.

FIG. 5 depicts a graph of a comparison of convergence speeds of the AFCcircuit of FIG. 1 at different settings in accordance with the presentembodiment.

And FIG. 6 depicts a graph of a comparison of root mean square errors oftracking results of the AFC circuit of FIG. 1 at different settings inaccordance with the present embodiment.

Skilled artisans will appreciate that elements in the figures areillustrated for simplicity and clarity and have not necessarily beendepicted to scale. For example, the dimensions of some of the elementsin the block diagrams or flowcharts may be exaggerated in respect toother elements to help to improve understanding of the presentembodiments.

DETAILED DESCRIPTION

The following detailed description is merely exemplary in nature and isnot intended to limit the invention or the application and uses of theinvention. Furthermore, there is no intention to be bound by any theorypresented in the preceding background of the invention or the followingdetailed description. It is the intent of the present embodiment topresent a novel non-decision-aided digital closed loop automaticfrequency control (AFC) tracking algorithm based on the moving averageof the digital discriminator's output. This AFC does not require timingrecovery and/or source data recovery. While existing decision-aided AFCmethods usually require received data to be demodulated even before CFOis compensated thereby limiting the tracking range to below the maximumfrequency deviation, the AFC in accordance with the present embodimentdoes not have such a requirement and thus has a wider tracking range. Anadaptive tracking loop gain scheme is also proposed to achieve a fasterand more accurate tracking. The scheme automatically switches betweenfast and accurate modes by adjusting the loop gain according to theestimated CFO for each iteration. Lastly, Inter-Channel Interference(ICI) and Automatic Gain Control (AGC) uncertainties are also taken intoconsideration by normalizing the in-phase (I) and quadrature (Q) samplesby re-using the existing digital RSSI. Meanwhile, the normalization isbased on a simple bit shift and truncation process which makes it easyto implement.

Referring to FIG. 1, a block diagram 100 of a continuous-phase frequencyshift keying (CPFSK) modulation device in accordance with a presentembodiment is depicted. While the block diagram 100 and the discussionof the AFC algorithm in accordance with the present embodiment islimited to a CPFSK modulation device, the present embodiment and its AFCalgorithm is applicable to any frequency modulation scheme includingFSK, GFSK, and MSK. It will also be clear to those skilled in the artthat the present embodiment based on a general CPFSK system can beeasily applied to other phase modulation schemes.

Assume the received CPFSK signal is not distorted by channel andpre-detection filter, it can be denoted as

$\begin{matrix}{{x(t)} = {{\sqrt{\frac{2\; E_{b}}{T_{b}}}{\cos\left( {{2\;\pi\; f_{c\;}t} + {\theta(t)} + \theta_{0}} \right)}} + {n(t)}}} & (1)\end{matrix}$where fc is the carrier frequency, n(t) is an additive bandpass Gaussiannoise with one-sided power spectrum density N₀, E_(b) and T_(b) are thebit energy and bit period respectively. It should be noted that E_(b) isthe bit energy with the effects of AGC and with ICI removed by low-passfilter. It varies with different AGC or different ICI. θ₀ is the initialphase offset and θ(t) is the frequency modulated phase as shown belowθ(t)

2πh∫ _(−∞) ^(t)Σ_(n=−∞) ^(∞) x[n]g(τ−nT)dτ  (2)where g(t) is the pulse shaping function for binary data, and h is themodulation index.

The block diagram 100 presents a device for closed-loop recursiveautomatic frequency calibration (AFC) algorithm for CFOestimation/compensation. The received waveform is received by an inputmodule 110 which down converts the signal x(t) (Equation 1) to producebaseband in-phase (I) and quadrature (Q) signals. Ignoring the noise,the signals can be expressed as

$\begin{matrix}{{I(t)} = {\sqrt{\frac{E_{b}}{2\; T_{b}}}{\cos\left( {{2\;\pi\;\Delta\; f\; t} + {\theta(t)} + \theta_{0}} \right)}}} & (3) \\{{Q(t)} = {\sqrt{\frac{E_{b}}{2\; T_{b}}}{\sin\left( {{2\;\pi\;\Delta\; f\; t} + {\theta(t)} + \theta_{0}} \right)}}} & (4)\end{matrix}$where Δf=f_(c)−f_(c)′ is the frequency offset, and f_(c)′ is thefrequency generated by the local oscillator.

The input module 110 then samples the I(t) and Q(t) signals at asampling rate 1/T_(s) and then normalized by a normalization module 112.The normalized samples are then passed to a digital discriminator 114 ofa carrier frequency offset (CFO) module 116. The delay taps of thedigital discriminator 114 are set to D and the discriminated signal isfiltered by a low pass filter (LPF) 118. Thereafter, a moving averageblock 120 with window size L_(w) generates an indication of thedifference between the transceiver's carrier frequency offset Δf.Lastly, the error signal is filtered by a loop filter module 122 tosmooth out the noise and is used to steer a phase lock loop (PLL) togenerate a frequency of a local oscillator 124 towards f_(c).

From FIG. 1, it can be imagined that the error signal is proportional tothe gains along the receiving path including AGC adjustment and low-passfilter for removing ICI. It is necessary to remove or suppress suchcorrelation by normalization. A received signal strength indicationmodule 126 provides a RSSI signal to the normalization module 112 fornormalization by reusing the digital RSSI signal.

The over-sampled version of the complex baseband signals given byEquations 3 and 4 can be written as

$\begin{matrix}{{I\lbrack k\rbrack} = {{I\left( {k\; T_{x}} \right)} - {\sqrt{\frac{E_{b}}{2\; T_{b}}}{\cos\left( {{2\;\pi\;\Delta\; f\; k\; T_{x}} + {\theta\left( {k\; T_{x}} \right)} + \theta_{0}} \right)}}}} & (5) \\{{Q\lbrack k\rbrack} = {{Q\left( {k\; T_{x}} \right)} = {\sqrt{\frac{E_{b}}{2\; T_{b}}}{\sin\left( {{2\;\pi\;\Delta\; f\; k\; T_{x}} + {\theta\left( {k\;{T\;}_{x}} \right)} + \theta_{0}} \right)}}}} & (6)\end{matrix}$

These I Q samples are passed to the discriminator 114 for furtheroperation. However, the discriminator 114 output is proportional to theoverall gains along the receiving path. Thus, the estimated CFO signaloutput from the CFO module 116 necessarily includes a constant ambiguitywhich needs to be removed. In accordance with the present embodiment,the normalization module 112 directly normalizes the I Q samples beforethey are fed to the discriminator 114. This advantageously narrows therequired dynamic range of the digital discriminator 114 and achieves astable tracking speed. In addition, reusing of the RSSI signal from theRSSI module 126 saves power consumption and chip area. In the digitalRSSI module 126, the signal power is estimated by a filter 128 filteringan average value of the powers of I Q samples calculated by block 130,i.e.,P=Σ _(k=0) ^(L) ^(R) ⁻¹ I ² [k]+Q ² [k]  (7)where L_(R) is the length of the observation window of the RSSI. Theestimated signal power is mapped to dB with resolution of 1 dB at a gainto dB block 132 and fed to the normalization module 112 to normalize theI Q samples. The normalization module 112 performs a dividing operationby truncating and shifting the fixed point samples. As each integervalue of RSSI corresponds to a number of bits and direction of I Qsamples (fixed-point numbers) that are to be shifted and the shiftednumbers are truncated in accordance with the normalization requirements,the normalization module 112 shift and truncation process is easilyimplemented. Furthermore, the I Q samples are normalized within acertain range so that the AFC will never loose convergence, given othernecessary conditions are satisfied.

Referring to FIG. 2, a graph 200 illustrates the output of the digitaldiscriminator 114 when the input I Q samples are normalized inaccordance with the normalization scheme of the present embodiment. TheI Q sample signal power is plotted along the x-axis 202 and the meansquare value of the output of the discriminator 114 is plotted along they-axis 204. The power of the input signal spans from 0 to 60 dB(normalized to Vpp=1V). Without normalization (trace 206) thediscriminator output increases as the I Q signal power increases.However, after normalization, it is clear that the discriminator 114output is limited in a reasonable range as seen in the bounded saw-bladepattern of trace 208.

The digital discriminator 114 outputs are the normalized I Q samples.Substituting this output into Equation 2 can be expressed as

$\begin{matrix}\begin{matrix}{{\xi\lbrack k\rbrack}\overset{\Delta}{=}{{{I\left\lbrack {k - D} \right\rbrack}{Q\lbrack k\rbrack}} - {{I\lbrack k\rbrack}{Q\left\lbrack {k - D} \right\rbrack}}}} \\{= {\sin\;\left\{ {{2\;\pi\;\Delta\; f\; D\; T_{s}} + \left\lbrack {{\theta\left( {k\; T_{s}} \right)} - {\theta\left( {{k\; T_{s}} - {D\; T_{s}}} \right)}} \right\rbrack} \right\}}} \\{= {\sin\left\{ {{2\;\pi\;\Delta\; f\; D\; T_{s}} + {2\;\pi\; h{\int_{{({k - D})}T_{s}}^{k\; T_{s}}{\sum\limits_{n = {- \infty}}^{\infty}{{x\lbrack n\rbrack}{g\left( {\tau - {n\; T}} \right)}d\;\tau}}}}} \right\}}} \\{= {\sin\;\left\{ {2\;{\pi\left\lbrack {{\Delta\; f\; D\; T_{s}} + {h\;{\varnothing\left( {k\; T_{s}} \right)}}} \right\rbrack}} \right\}}}\end{matrix} & (8)\end{matrix}$where Ø(kT_(s)) is defined byØ(kT _(s))

∫_((k−D)T) _(s) ^(kT) ^(s) Σ_(n=−∞) ^(∞) x[n]g(τ−nT)dτ  (9)

For the average of the above discriminator 114 output ξ[k] with anobservation window size being L_(w) samples, if the preamble satisfiesthe condition of (0,1) balance in the observation window, then theaverage output can be expressed as

$\begin{matrix}\begin{matrix}{\overset{\_}{\xi}\overset{\Delta}{=}{{\frac{1}{L_{w}}{\sum\limits_{k = l}^{L_{w} + l - 1}{{I\left\lbrack {k - D} \right\rbrack}{Q\lbrack k\rbrack}}}} - {{I\lbrack k\rbrack}{Q\left\lbrack {k - D} \right\rbrack}}}} \\{= {\frac{1}{L_{w}}{\sum\limits_{k = l}^{L_{w} + l - 1}{\sin\left\{ {2\;{\pi\left\lbrack {{\Delta\; f\; D\; T_{s}} + {h\;{\varnothing\left( {k\; T_{s}} \right)}}} \right\rbrack}} \right\}}}}} \\{= {\frac{1}{L_{w}}\left\{ {{{\sin\left( {2\;\pi\;\Delta\; f\; D\; T_{s}} \right)}{\sum\limits_{k = l}^{L_{w} + l - 1}{\cos\left\lbrack {2\;\pi\; h\;{\varnothing\left( {k\; T_{s}} \right)}} \right\rbrack}}} +} \right.}} \\\left. {{\cos\left( {2\;\pi\;\Delta\; f\; D\; T_{s}} \right)}{\sum\limits_{k = l}^{L_{w} + l - 1}{\sin\left\lbrack {2\;\pi\; h\;{\varnothing\left( {k\; T_{s}} \right)}} \right\rbrack}}} \right\}\end{matrix} & (10)\end{matrix}$

Under the condition of the preamble being (0,1) balanced, it can beproven that the first summation term of Equation 10 in the big bracketcan be approximated by a positive constant, and the second summationterm approximately equals zero, that isΣ_(k=l) ^(L) ^(w) ^(+l−1) cos [2πhØ(kT _(s))]≈α  (11)Σ_(k=l) ^(L) ^(w) ^(+l−1) sin [2πhØ(kT _(s))]≈0  (12)Therefore in view of the above and taking the AWGN noise intoconsideration, the moving average calculated at the block 120 and inEquation 10 can be rewritten as

$\begin{matrix}{{\overset{\_}{\xi} \approx {{\frac{\alpha}{L_{w}}{\sin\left( {2\;\pi\;\Delta\; f\; D\; T_{s}} \right)}} + {w(n)}}} = {{2\;\pi\;\beta\;\Delta\; f\; D\; T_{s}} + {w^{\prime}(n)}}} & (13)\end{matrix}$

where w(n) refers to the effect of AWGN noise, and w′(n) is the overallnoise including the approximation error. Thus, the frequency offsetoutput from the CFO module 116 can be estimated by

$\begin{matrix}{{\Delta\;\hat{f}} = {\frac{2}{2\;\pi\;\beta\; D\; T_{s}}\overset{\_}{\xi}}} & (14)\end{matrix}$

The approximation in Equation 13 holds only when 2πΔfDT_(s) is small.But when the feed-back tracking loop of the module 122 is employed, thecondition for the loop to converge is that sin(2πΔfDT_(s)) has the samesign as Δf to prevent the estimation of CFO being tracked to a wrongdirection. Therefore, the condition of convergence is |2πΔfDT_(s)|<π,that is

$\begin{matrix}{{- \frac{1}{2\; D\; T_{s}}} < {\Delta\; f} < \frac{1}{2\; D\; T_{s}}} & (15)\end{matrix}$

The structure of the tracking loop filter 122 with adaptive gain isbased on a standard feedback loop except that the loop gain in the loopfilter 122 is adaptive to achieve fast tracking speed as well asaccuracy. Since the parameter β in Equation 14 would be affected byfactors such as sample timing error, a closed-loop recursive method isimplemented to avoid this problem. The overall gain 1/(2πβDT_(s)) isabsorbed into the loop gain K_(p) and it should be noted that K_(p) hasa certain range of tolerance for tracking convergence. Hence thetracking speed and accuracy is not very sensitive to the error of theparameter β.

The loop filter 122 has two working modes, a Fast Mode and an AccurateMode. It automatically switches between these two modes according to theabsolute value of the moving average ξ[k] for each iteration as shown inFIG. 1. A positive threshold ζ is preset. The tracking loop is switchedto Fast Mode or Accurate Mode if |ξ[k]| is detected above or below ζ,respectively. The mode switching is achieved by selecting High or Lowvalues of the loop gain K_(p). It is set to a high value K_(h) in theFast Mode, and set to a low value K_(l) in the Accurate Mode, whereK_(h) and K_(l) are preset for each system.

In noisy cases, it is possible that ξ[k] jumps between above ζ and belowζ, respectively, for some consecutive iterations. To avoid such gainoscillation, the mode switching can be limited to happen only when ξ[k]is stabilized after switching from one side of the threshold to another.FIG. 3 depicts a flowchart 300 illustrating the logic of this gaincontrol. At step 302, ξ[k] is received and at step 304 it is detectedwhether |ξ[k]| is above or below ζ. When |ξ[k]| is above ζ 304, thelogic operates in the Fast Mode and it is decided 306 whether theprevious ξ[k] (i.e., |ξ[k]−1|) is greater than ζ, i.e., whether theoperating mode is already in the Fast Mode. If operation is already inthe Fast Mode 306, the counter is incremented 308 and it is determined310 whether the number of samples is equal to the observation windowsize, that is whether the counter equals L_(c). If operation is not inthe Fast Mode 306, the counter is initialized to zero 312 and it isdetermined 310 whether the counter equals L_(c). When the counter equalsL_(c) 310, the number of samples is equal to the observation window sizeand the loop gain K_(p) is set equal to the high value K_(h) 314 in theFast Mode and the signal is filtered by multiplying ξ[k] by K_(p) 316.

Alternatively, when |ξ[k]| is below ζ 304, the logic operates in theAccurate Mode and it is decided 318 whether the previous ξ[k] (i.e.,|ξ[k]−1|) is higher than ζ, i.e., whether the operating mode is in theFast Mode. If operation is in the Fast Mode 318, the counter isinitialized to zero 320 and it is determined 322 whether the counterequals L_(c). If operation is already in the Accurate Mode 318, thecounter is incremented 324 and it is determined 322 whether the numberof samples is equal to the observation window size, that is whether thecounter equals L_(c). When the counter equals L_(c) 322, the number ofsamples is equal to the observation window size and the loop gain K_(p)in the Accurate Mode is set equal to the low value K_(l) 326 and thesignal is filtered by multiplying ξ[k] by K_(p) 316.

The following Table 1 shows the comparison of the main features of theAFC circuit of FIG. 1 and conventional AFC implementations.

TABLE 1 Present Parameters Embodiment 1^(st) Prior Art 2^(nd) Prior Art3^(rd) Prior Art 4^(th) Prior Art IF 172.8 MHz 8 MHz 8 MHz N.A. 910 MHzData Rate 1.2-4.8 MHz 3 MHz 3 MHz 0.7-2.1 MHz 1 MHz Over Sampling 9x 8x8x N.A. 4x Converge Speed 16 bits >32 bits >32 bits N.A. N.A. Mod MultiGFSK GFSK GFSK GFSK Dec-Aid No Yes Yes N.A. No Norm Yes No No Yes NoAdaptive Gain Yes No No No No Trackable Δf/R_(b) ±4.5 ±0.033 ±0.033 N.A.±0.24 Residual Δf 2.4% 3.0% 3.5% N.A. 3.9% SNR = 10 dB

From the contents listed in Table 1, it can be concluded that the AFCalgorithm in accordance with the present embodiment has attractivefeatures in the areas of tracking speed, accuracy, and trackable range.The normalization scheme by reusing the existing digital RSSI and theshifting/truncating-based process to fixed point samples maintainsbalance between the performance and system complexity. The I Q samplesare normalized to within a certain range by simple MSB searching, bitshifting and truncation. Further, the non-decision-aided CFO estimationalgorithm can advantageously achieve a wider trackable range because theCFO is estimated by the moving average of the discriminator outputs anddoes not require timing recovery nor need to reconstruct transmittedsymbols. In this manner, CFO estimation in accordance with the presentembodiment has no limitation for its tracking range to be less than themaximum frequency deviation. The trackable CFO range is identifiedmathematically in Equation 15, above, and the advantages of this featureare proven by the contents listed in Table 1. Also, by automaticallyswitching the adaptive loop gain between High and Low values accordingto the estimated CFO for each iteration, the loop gain is switchedbetween Fast and Accurate modes, advantageously providing a highertracking speed and better performance.

Referring to FIG. 4, a graph 400 depicts the AFC tracking results forthe present embodiment of FIG. 1 with and without adaptive gain controland two different observation window length (18 and 36) are considered.The number of samples (i.e., time) is plotted along the x-axis 402 andthe frequency is plotted along the y-axis 404 where the target CFOfrequency is shown by the true CFO trace 406. It can be observed thatthe convergence speed of the system with adaptive gain control (traces410, 414) in accordance with the present embodiment to the true CFO 406is faster than that with constant loop gain (traces 412, 416). The graph400 also shows the effects of the observation window length on thetracking speed. On one hand, the larger the window size 36 (traces 412,414), the higher the estimation accuracy. On the other hand, the largerwindow size also slows down the tracking speed. Therefore estimationaccuracy and tracking speed has to be balanced.

Referring to FIG. 5, a graph 500 depicts the convergence rate wheresignal-to-noise ratio (SNR) is plotted along the x-axis 502 and thenumber of samples needed to converge is plotted along the y-axis 504. Itcan be seen from the graph 500 that the adaptive gain simulations(traces 506, 508) take less samples to converge than constant gain(traces 510, 512).

Lastly, in FIG. 6, a graph 600 depicts the tracking performance of theproposed AFC with different settings where SNR is plotted along thex-axis 602 and Root Mean-Square (RMS) Error is plotted along the y-axis604. The adaptive gain simulations are plotted on traces 608, 612 andthe constant gain simulations are plotted on traces 606, 610. Theperformance of a prior art decision-aided AFC is also simulated forcomparison on trace 614. In graph 600 it is shown that the proposedalgorithm (traces 608, 612) achieves better performance than thedecision-aided algorithm 614. This is because the performance of thedecision-aided AFC 614 depends on the accuracy of timing recovery. Giventhe fact that there exists some random timing error, decision-aided AFCnecessarily suffers some performance degradation.

Thus, in accordance with the present embodiment, an advantageous, robustmoving average based AFC tracking algorithm has been presented whichovercomes the drawback of the prior art. This algorithm is an easy toimplement AFC scheme which does not require timing recovery and/orsource data recovery and also takes into account the ICI and AGCuncertainties. The present embodiment can be applied to any CPFSKsystems. The carrier frequency offset is estimated in the CFO module 116by averaging at the block 120 the digital discriminator 114 output. Anadaptive tracking loop with auto-switching loop gain is provided by theloop filter 122 to achieve higher tracking speed and accuracy. Inaddition, a simple normalization scheme with the assist of the existingdigital RSSI 126 by shifting and truncation is provided to narrow downthe required dynamic range the digital discriminator 114 and remove theeffects of inter-channel interference and AGC uncertainties. Theautomatic loop gain control scheme is a simple comparing and switchingprocess which is valuable to further optimize the threshold and thevalues of High and Low gains. While exemplary embodiments have beenpresented in the foregoing detailed description of the invention, itshould be appreciated that a vast number of variations exist. Forexample, those skilled in the art will realize from the teachings hereinthat the present technology may also be applied to any frequencymodulation scheme including FSK, GFSK, and MSK.

It should further be appreciated that the exemplary embodiments are onlyexamples, and are not intended to limit the scope, applicability,operation, or configuration of the invention in any way. Rather, theforegoing detailed description will provide those skilled in the artwith a convenient road map for implementing an exemplary embodiment ofthe invention, it being understood that various changes may be made inthe function and arrangement of elements and method of operationdescribed in an exemplary embodiment without departing from the scope ofthe invention as set forth in the appended claims.

What is claimed is:
 1. A method for automatic frequency control (AFC)without bit timing recovery comprising: normalizing each sample ofbinary elements of a received signal to an input amplitude of the eachsample by bit shifting the each sample of the binary elements or bytruncating the each sample of the binary elements to enhance a dynamicrange of the binary elements; and calculating a frequency discriminationfor the each sample of the binary elements and a moving average of thebinary elements of the received signal in an adaptive control loop untilthe AFC converges, wherein the adaptive control loop tracks loop gainby: utilizing multiple estimations of a carrier frequency offset (CFO)estimation, adjusting the loop gain in response to the estimated CFO,and repeating the estimating and adjusting steps until accuratecorrection is achieved.
 2. The method in accordance with claim 1 whereinthe step of calculating the moving average comprises calculating thefrequency discrimination and the moving average of the binary elementsof the signal without demodulating the signal.
 3. An automatic frequencycontrol (AFC) device comprising: an input module for sampling a receivedsignal and normalizing each sample of binary elements of the receivedsignal to an input amplitude of the each sample of the binary elementsby bit shifting the each sample of the binary elements or by truncatingthe each sample of the binary elements to enhance a dynamic range of thebinary elements; a carrier frequency offset (CFO) estimation modulecoupled to the input module for calculating a frequency discriminationfor each of the binary elements and a moving average of the binaryelements in an adaptive control loop, the CFO estimation modulecontinuing to calculate the moving average until the AFC converges; andan adaptive loop filter coupled to the CFO estimation module forperforming an adaptive tracking loop gain utilizing multiple estimationsof the CFO from the CFO estimation module, wherein the adaptive loopfilter has two operational modes, switches between the operational modesin response to the estimations from the CFO estimation module, andrepeatedly performs the adaptive tracking the loop gain in response toan estimated CFO signal received from the CFO estimation module untilaccurate tracking is achieved.
 4. The AFC device in accordance withclaim 3 wherein the CFO estimation module receives an undemodulatedreceived signal from the input module, the CFO estimation modulecalculating the moving average of the binary elements of the signalwithout demodulating the undemodulated received signal.
 5. A method forautomatic frequency control (AFC) without bit timing recoverycomprising: normalizing each sample of binary elements of a receivedsignal to an input amplitude of the each sample by bit shifting the eachsample of the binary elements or by truncating the each sample of binaryelements to enhance a dynamic range of the binary elements; andcalculating a frequency discrimination for the each sample of the binaryelements and a moving average of the binary elements of the receivedsignal without demodulating the signal in an adaptive control loop untilthe AFC converges, wherein the adaptive control loop tracks loop gainby: utilizing multiple estimations of a carrier frequency offset (CFO)estimation, adjusting the loop gain in response to the estimated CFO,and repeating the estimating and adjusting steps until accuratecorrection is achieved.